---
title: "Example scenarios"
output:
  rmarkdown::html_vignette:
    toc: true
    toc_depth: 6
vignette: >
  %\VignetteIndexEntry{Example scenarios}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(max.print=50)
data.table::setDTthreads(1)
```


# Introduction

European Forestry Dynamics Model (EFDM) is a model for large scale scenario analysis. The model is an area-based matrix model where forest area is stratified with respect to, for example, use, management strategy  and development (growth). The model can consider also other land uses, see Scenario 3. In a model run forest area moves in 'a matrix' span with the stratifying factors.

The `efdm` package includes functions to

  - define management activities
  - estimate transition probabilities
  - run an EFDM scenario

and an example dataset for illustration purposes.

In this vignette, we demonstrate 'efdm' in running a scenario with the example dataset.
Typical workflow in a EFDM scenario analysis starts with preparing the datasets required as an input. Preparing the datasets is usually the most time consuming part of the scenario analysis, and the work flow depends on the data available. Therefore, the preparation is not explained in this vignette. However, the attached datasets offer an example for preparing own datasets.


The `efdm` is loaded with
```{r}
library(efdm)
```

The following packages are used in this vignette to manipulate data and plot results.
However, the data manipulation and reporting the results could be done using other packages or even outside the R environment.
```{r setup, warning = FALSE, message = FALSE}
library(dplyr)
library(ggplot2)
library(sf)
```


# Example data

In this vignette we are using `example`, a collection of example datasets to
describe working with `efdm`. The dataset covers entire Finland and the initial state represents year 2016.
However, the dataset is a toy data which is meant for learning the model concept.
```{r}
data(example)
```

In this example we have chosen

  - a 5-year simulation step
  - age and total growing stock volume per hectare to present forest state
	- age is classified into 5 year classes, starting from class 1 = 0-5 years, and ending at class 35 = 170+ years with all forests over 170 years old
	- volume is classified into 15 classes with class limits depending on the dominant tree species and their growth pattern
  - additional stratification variables defining different forest strata are spatial region (North, Middle, South), soil type (Mineral, Peat) and dominant tree species (spruce, other species)

# Activities

In EFDM forest management is represented by activities and activity probabilities.
In the first example we have three activities

 - thinning (thin)
 - final felling (ff)
 - no management (noman)

## Activity probability and transition probabilities

The activity probabilities (`actprob` below) are the probabilities for applying a specific
management activity in a specific forest state described by the stratification variables.
The activity probabilities are used to allocate forest area to different activities.
A valid activity probability specifies activity probabilities for all possible states and activities.
All probabilities should be positive and the sum of activity probabilities for each state should be 1.
```{r}
actprob <- example$actprob
actprob
```

Each activity has a transition probability matrix.
The transition probability describes movements of forest areas by the simulation time steps.
It is used to move a forest state to the next states.
A valid transition probability should specify transition probability for each state where the activity can be used.
For example if final felling only applies to forests with some minimum volume, then there is no need to specify transitions from states with less than the minimum volume.

## Final felling

The final felling activity is represented by a transition matrix that has
probability 1 to move any forest to a state where volume and age are in
the smallest class.

The target state is independent of the starting state. It will always be `vol=1` and `age=1`, the smallest class in the stratification.
```{r}
transprobs_ff <- expand.grid(vol0=1:15, age0=1:35, vol1=1, age1=1, prob=1)
transprobs_ff
```
In `efdm` the final felling activity is defined by giving

  - name of the activity, "ff"
  - names of the dynamic variables, that is the variables that the final felling activity affects, "vol" and "age"
  - transition probabilities

```{r ff}
ff <- define_activity("ff", c("vol", "age"), transprobs_ff)
```


## No management

If there are no management activities or treatments in the forest, "no management" activity is
used to make the forest grow. In this example, we assume that growth changes the
values of volume and age and is dependent on region, soil type and tree species.
Moreover, we assume that the growth is so different in between regions and tree
species, that it should be estimates completely separately for each class.

### Estimation of transition matrix using pair data

To estimate the transition matrix, we use "pair data" obtained from two
consecutive measurements of permanent sample plots in the national forest inventory.
For `estimatetransprobs`, the estimation function implemented
in `efdm`, the pair data is a `data.frame` of pairs of dynamic variables and
possible stratification variables, which affect the growth but are not affected
by growth.
```{r pairdata}
example$noman_pairs
```

In order to use `estimatetransprobs` we still need two parts, a state space and
a prior.
The state space and prior are required because the pair data might not have observations
for all possible states, but the transition matrix should be available for all
states.

#### State space

The state space is the collection of all possible states of stratifying variables.
Since we have an activity probability for each state we can use the `actprob` to
obtain the `statespace`

```{r statespace}
statespace <- actprob %>% select(c(region, soil, sp, vol, age))
statespace
```
In this example dataset the number of states in the state space is the product of the number of classes in each stratifying variable, that is $3*2*2*15*35 = 6300$.

#### Prior

The prior in `estimatetransprobs` works by adding one observation to the pair data
for each starting state.
The `efdm` has a few standard prior choices.

  - `prior_grow(variable)` grows `variable` by one class.
  - `"nochange"`, forest state is not changed
  - `"uninformative"`, an observation with tiny weight is added to every possible
  target state.

For the no management activity we are using `prior_grow("age")` stating
that there is always at least one observation where the age grows by one class
but the volume is not changing.


#### Estimation

Now we are ready to estimate the transition probabilities for no management
```{r}
transprobs_noman <- estimatetransprobs(c("vol", "age"), # the dynamic variables of the activity
                     example$noman_pairs,  # pair data for no management
                     statespace=statespace,
                     factors=c("soil"), # Information for different soil types
                                        # is used with smaller weight
                     by=c("region","sp"), # Separate estimation for each
                                          # region and species
                     prior=prior_grow("age"))
```
and to define the activity `noman`
```{r noman}
noman <- define_activity("noman", c("vol", "age"), transprobs_noman)
```

The resulting transition probabilities look like
```{r}
transprobs_noman %>%
  arrange(vol0, age0, soil, region, sp, vol1, age1)
```

## Thinning

We use exactly the same procedure for thinning that we used for no management.
The only differences are the activity name and the pair data used.

```{r thin}
transprobs_thin <- estimatetransprobs(c("vol", "age"),
                     example$thin_pairs, # pair data for thinning
                     statespace=statespace,
                     factors=c("soil"),
                     by=c("region","sp"),
                     prior=prior_grow("age"))
thin <- define_activity("thin", c("vol", "age"), transprobs_thin)
```


# Scenario 1

EFDM is an area based model. The initial state of the scenario is a `data.frame`
where for each state there is an `area` (zeros may be omitted)
```{r}
state0 <- example$initial_state
state0
```

Next, we run the EFDM scenario for 20 time steps (100 years) starting with
initial state `state0`, activity probabilities `actprob` and a list of activities.
```{r}
activities <- list(noman, thin, ff)
states1 <- runEFDM(state0, actprob, activities, 20)
```

`runEFDM` produces a `data.frame` of areas allocated to each activity at each time step.

```{r}
states1 %>%
  arrange(soil, region, sp, vol, age, time, activity)
```

## Analysing results

### Result coefficients

Using so called result coefficients is a way to obtain results with respect to other forest properties than area.
In this scenario we have growing stock volume, drain and harvest income as result variables.

The volume coefficients convert volume class `vol` and dominant species `sp` into growing stock volume `volume` (m3/ha) in four tree species groups (`species`): pine, spruce, broadleaves and all.
The volume coefficients were estimated for the classes based on the species composition in the forest inventory data as class averages.

```{r}
example$vol_coef %>%
  arrange(vol, sp, species)
```

Drain is the harvest accumulation which is linked to management activities (activity), and separated by the volume class (vol) and dominant species (sp). It was also estimated from the observed changes in the forest inventory data as class averages.
The drain is given as m3 per ha (drain) and timber assortment (assort): pulp/saw wood.
```{r}
example$drain_coef %>%
  arrange(-vol, sp, assort, activity)
```

Income is loosely based on the actual statistics of Finnish timber assortment prices over last years in unit eur/m3.
```{r}
example$income_coef
```

### Results
Simulation timesteps are mutated to mid-years of simulation steps (the simulation begins from year 2016):
```{r}
states1 <- states1 %>% mutate(time = factor(2016 + time*5))
```


Growing stock volume is estimated by merging the area distribution by simulation steps (states1) and volume coefficients. The total growing stock volume (m3) is a result of multiplication of area (ha) and coefficient (m3/ha). Finally the result is visualized in a figure.
```{r}
volume <- merge(states1, example$vol_coef) %>%
  mutate (volume=area*volume) %>% #area is multiplied with m3/ha volume the get total volume in m3
  filter(species != "all")
ggplot(volume) +
  scale_fill_viridis_d(end = 0.9) +
  geom_bar(aes(x=time, weight=volume/1000000000, fill=species)) +
  labs(y=NULL,title=expression(paste("Growing stock, bil.",m^3)), x="Year", fill="") +
  theme(legend.position = "bottom", axis.text.x = element_text(angle = -90))
```

Estimating age distribution does not require result coefficients. However, the age classes are defined for 50-year classes instead of original 5-year classes, and then plotted for only three time steps: years 2016, 2066 and 2116.
```{r}
states1$ageclass <- cut(states1$age, breaks=c(0,10,20,30,35), include.lowest = TRUE, 
                        #labels=c("0-50","51-100","101-150","150+"))
                        labels=c("-50","-100","-150","150+"))
states1$region <- factor(states1$region, labels = c("South", "Middle", "North"))
ggplot(subset(states1, time %in% c(2016,2066,2116))) +
  geom_bar(aes(x=ageclass, weight=area/1000000, fill=region)) +  
  scale_fill_viridis_d(end = 0.9) +
  facet_grid(cols=vars(time)) +
  labs(y=NULL, title="Area, mill.ha", x="Ageclass", fill=NULL) +
  theme(legend.position = "bottom", axis.text.x = element_text(angle = -90))
```


Income is estimated by first converting the drain (m3/ha) into income (eur/ha). Then multiplication with area (ha) gives the euros.
```{r}
euro <- merge(example$drain_coef, example$income_coef) %>% mutate(euro = euro*drain)
removal <- merge(states1, euro) %>% mutate(income=euro*area)

ggplot(subset(removal,!time %in% c(2116))) + 
  geom_bar(aes(x=time, weight=income/5000000000, fill=assort)) +
  scale_fill_viridis_d(end = 0.9) +
  labs(y=NULL,title = "Income, bil.€/year", x="5-year intervals", fill=NULL ) +
  theme(legend.position = "bottom", axis.text.x = element_text(angle = -90))
```





# Scenario 2
In this example the tree species changes after final felling. Therefore we redefine final felling activity taking into account in addition to volume and age also the dominant species. The change probability depends on region and dominant species.
Volume and age act as before. They move to the smallest classes (vol1=1 and age1=1).
```{r}
transprobs_ff_age_species <- statespace %>%
  select(vol0=vol, age0=age, sp0=sp, region) %>%
  unique %>%
  group_by(region, sp0, vol0, age0) %>%
  summarize(data.frame(vol1=1, age1=1, sp1=c('other','spruce'),
                       prob=case_when(
                         sp0=='other' && region=='South' ~ c(1, 0),
                         sp0=='other' && region=='Middle' ~ c(1, 0),
                         sp0=='other' && region=='North' ~ c(0.8, 0.2), #in other dominated forest
                                        # in northern boreal vegetation zone 20% of final felled area
                                        # change to spruce dominated forest
                         sp0=='spruce' && region=='South' ~ c(0.3, 0.7),
                         sp0=='spruce' && region=='Middle' ~ c(0.2, 0.8),
                         sp0=='spruce' && region=='North' ~ c(0, 1))))
ff_age_species <- define_activity("ff", c("vol", "age", "sp"), transprobs_ff_age_species)
```

The list of activities includes the same noman and thin as in Scenario 1, and the above defined final felling. The same initial state as in Scenario 1 is run with the new list of activities and stored in variable states2.
```{r}
activities2 <- list(noman, thin, ff_age_species)
states2 <- runEFDM(state0, actprob, activities2, 20)
```


The proportion of spruce dominated forest area is estimated for time steps 0, 10 and 20 (years 2016, 2066 and 2116). And the result is presented by the vegetation zones as a map.

```{r}
# Compute proportion of spruces in each time and region
prop_spruces <- states2 %>%
  group_by(region, time) %>%
  summarise(proportion = 100*sum((sp=="spruce")*area)/sum(area)) %>%
  filter(time %in% c(0, 10, 20)) %>%
  mutate(time = 2016 + 5*time)

# Add bio-geographical regions (MetsaKasvVyoh provided in efdm package) to the data
prop_spruces <- merge(MetsaKasvVyoh, prop_spruces)
prop_spruces %>% ggplot() +
  geom_sf(aes(fill=proportion)) +
  facet_wrap(vars(time)) +
  scale_fill_gradient(low = "white", high = "forestgreen") +
  labs(fill="Proportion" ,title="Proportion of spruce dominated forests, %") +
  guides(fill = guide_colourbar(frame.colour = "grey20",ticks.colour = "grey20")) +
  theme(legend.position = "bottom", axis.text.x = element_text(angle = -90))
```

The map provided by: Finnish Environment Institute

# Scenario 3 - Land use changes

First we add two land use classes to the statespace. Agriculture is stratified according to region and soil type, while other land use is only stratified according to region.
```{r}
statespace3 <- statespace
statespace3$landuse <- "forest"
agriculture <- unique(statespace3 %>% mutate(sp=0, vol=0, age=0, landuse="agriculture"))
other <- unique(statespace3 %>% mutate(soil=0, sp=0, vol=0, age=0, landuse="other"))
statespace3 <- rbind(statespace3, agriculture, other)
```

We use separate activities for deforestation to each land use class.
Variables (vol, age, sp) not used by the agriculture are set to 0.
Soil type and region are not changing as a result of deforestation to agriculture.
```{r}
transprobs_defor_to_agri <- statespace3 %>%
  filter(landuse=="forest") %>%
  select(vol0=vol, age0=age, sp0=sp,landuse0=landuse) %>%
  unique %>%
  mutate(vol1=0, age1=0, sp1=0, landuse1="agriculture", prob=1)
defor_to_agri <- define_activity("defor_to_agri",
                                 c("vol", "age", "sp", "landuse"),
                                 transprobs_defor_to_agri)
```

Deforestation to other land use also changes soil type to 0.
```{r}

transprobs_defor_to_other <- statespace3 %>%
  filter(landuse=="forest") %>%
  select(soil0=soil, vol0=vol, age0=age, sp0=sp,landuse0=landuse) %>%
  unique %>%
  mutate(soil1=0, vol1=0, age1=0, sp1=0, landuse1="other", prob=1)
defor_to_other <- define_activity("defor_to_other",
                                  c("soil", "vol", "age", "sp", "landuse"),
                                  transprobs_defor_to_other)
```

Afforestation only applies to agriculture.
Volume and age classes start from 1 and the area is split evenly to spruce and other species.
```{r}
transprobs_aff <- statespace3 %>%
  filter(landuse=="agriculture") %>%
  select(soil, vol0=vol, age0=age, region, sp0=sp, landuse0=landuse) %>%
  mutate(vol1=1, age1=1, sp1="other", landuse1="forest", prob=1)
transprobs_aff <- rbind(transprobs_aff %>% mutate(sp1="spruce", prob=0.5),
                        transprobs_aff %>% mutate(sp1="other", prob=0.5))
affor <- define_activity("affor",
                         c("vol", "age", "sp", "landuse"),
                         transprobs_aff)
```

A donothing activity is used for non-forest land uses, when there is nothing forestry related going on.
```{r}
donothing <- define_activity("donothing", character())
activities3 <- list(noman, thin, ff, defor_to_other, defor_to_agri, affor, donothing)
```

adding land use information to state 
```{r}
state03 <- state0
state03$landuse <- "forest"
state03 <- rbind(state03,  agriculture %>% mutate(area=rep(c(1552000/2,997000/2,76000/2),each=2)),
                 other %>% mutate(area=c(721000,507000,112000)))
```

Activity probabilities for new land uses 
```{r}
actprob3 <- actprob
actprob3$landuse <- "forest"
# Define probabilities for new activities
actprob3$defor_to_other <- 0.0002
actprob3$defor_to_agri <- 0.00025
actprob3$affor <- 0
actprob3$donothing <- 0
# Normalise probabilities after adding new activities
actnames <- c("noman", "thin", "ff", "defor_to_other", "defor_to_agri", "affor", "donothing")
actprob3[actnames] <- actprob3[actnames]/rowSums(actprob3[actnames])
# Define activity probabilities also for agriculture and other land uses
actprob3 <- rbind(actprob3, 
        agriculture %>% mutate(thin=0, ff=0, noman=0, defor_to_other=0, defor_to_agri=0, affor=0.0005, donothing=0.9995),
        other %>% mutate(thin=0, ff=0, noman=0, defor_to_other=0, defor_to_agri=0, affor=0, donothing=1))
```


```{r}
states3 <- runEFDM(state03, actprob3, activities3, 20)
```

```{r}
#pie charts of land use areas
LUareas <- states3 %>%
  group_by(time, landuse) %>%
  summarise(area = sum(area)) %>%
  ungroup()
LUareas <- LUareas %>%
  mutate(time = factor(time, labels = seq(2016,2116,5)))
totalarea <- sum(state03$area)
LUareas %>% filter(time %in% c("2016","2066","2116")) %>%
  mutate(area = area/totalarea) %>%
  ggplot() +
  geom_bar(aes(x="",y=area, fill=landuse), stat="identity", width=1, color = "white") +
  scale_fill_viridis_d(end = 0.9) +
  coord_polar("y", start=0) +
  facet_wrap(vars(time)) +
  labs(y=NULL,fill="Land use", x=NULL)+
  theme(axis.text.x = element_blank(),
        axis.ticks = element_blank()) +
  geom_text(aes(x="",y = rep(c(0.03, 0.33, 0.95), 3), label = round(area*100,1)),
            nudge_x=0.25, size=2.25,
            color=rep(c("grey20","white","white"), 3), fontface="bold") +
  theme(legend.position = "bottom")
```
